OpenAI

This guide covers how to send OpenAI usage data to Fenra.

Chat Completions (GPT-4, GPT-3.5, o1, o3)

import OpenAI from 'openai';

const openai = new OpenAI();

async function chat(messages) {
  const response = await openai.chat.completions.create({
    model: 'gpt-4o',
    messages
  });

  // Send to Fenra
  await fetch('https://ingest.fenra.io/usage/transactions', {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'X-Api-Key': process.env.FENRA_API_KEY
    },
    body: JSON.stringify({
      provider: 'openai',
      model: response.model,
      usage: [{
        type: 'tokens',
        metrics: {
          input_tokens: response.usage.prompt_tokens,
          output_tokens: response.usage.completion_tokens,
          total_tokens: response.usage.total_tokens
        }
      }],
      context: {
        billable_customer_id: process.env.BILLABLE_CUSTOMER_ID
      }
    })
  });

  return response;
}

from openai import OpenAI
import requests
import os

client = OpenAI()

def chat(messages):
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=messages
    )

    # Send to Fenra
    requests.post(
        'https://ingest.fenra.io/usage/transactions',
        headers={
            'Content-Type': 'application/json',
            'X-Api-Key': os.getenv('FENRA_API_KEY')
        },
        json={
            'provider': 'openai',
            'model': response.model,
            'usage': [{
                'type': 'tokens',
                'metrics': {
                    'input_tokens': response.usage.prompt_tokens,
                    'output_tokens': response.usage.completion_tokens,
                    'total_tokens': response.usage.total_tokens
                }
            }],
            'context': {
                'billable_customer_id': os.getenv('BILLABLE_CUSTOMER_ID')
            }
        }
    )

    return response

Prompt Caching

When the response includes prompt_tokens_details.cached_tokens, include it:

usage: [{
  type: 'tokens',
  metrics: {
    input_tokens: response.usage.prompt_tokens,
    output_tokens: response.usage.completion_tokens,
    total_tokens: response.usage.total_tokens,
    cached_tokens: response.usage.prompt_tokens_details?.cached_tokens || 0
  }
}]

Reasoning Models (o1, o3)

OpenAI’s reasoning models include reasoning_tokens. Include them for accurate cost tracking:

usage: [{
  type: 'tokens',
  metrics: {
    input_tokens: response.usage.prompt_tokens,
    output_tokens: response.usage.completion_tokens,
    total_tokens: response.usage.total_tokens,
    reasoning_tokens: response.usage.completion_tokens_details?.reasoning_tokens || 0
  }
}]

Batch and Priority Tiers

OpenAI offers different pricing tiers. Specify the tier using model_tier:

{
  provider: 'openai',
  model: 'gpt-4o',
  model_tier: 'batch',  // Options: 'batch', 'priority', 'flex', 'standard'
  usage: [{
    type: 'tokens',
    metrics: {
      input_tokens: response.usage.prompt_tokens,
      output_tokens: response.usage.completion_tokens,
      total_tokens: response.usage.total_tokens
    }
  }],
  context: {
    billable_customer_id: process.env.BILLABLE_CUSTOMER_ID
  }
}

Multimodal Tokens (Realtime API)

For realtime and multimodal models, track separate token types:

usage: [{
  type: 'tokens',
  metrics: {
    input_tokens: response.usage.prompt_tokens,
    output_tokens: response.usage.completion_tokens,
    total_tokens: response.usage.total_tokens,
    text_tokens: response.usage.prompt_tokens_details?.text_tokens || 0,
    audio_tokens: response.usage.prompt_tokens_details?.audio_tokens || 0,
    image_tokens: response.usage.prompt_tokens_details?.image_tokens || 0
  }
}]

Image Generation (DALL-E)

const response = await openai.images.generate({
  model: 'dall-e-3',
  prompt: 'A sunset over mountains',
  n: 1,
  size: '1024x1024',
  quality: 'hd'
});

await fetch('https://ingest.fenra.io/usage/transactions', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
    'X-Api-Key': process.env.FENRA_API_KEY
  },
  body: JSON.stringify({
    provider: 'openai',
    model: 'dall-e-3',
    usage: [{
      type: 'images',
      metrics: {
        generated: response.data.length,
        size_px: 1024,
        quality: 'hd'  // Options: 'standard', 'hd' for DALL-E
      }
    }],
    context: {
      billable_customer_id: process.env.BILLABLE_CUSTOMER_ID
    }
  })
});

GPT Image Models

For GPT Image 1.5 and GPT Image 1, include quality and dimensions:

const response = await openai.images.generate({
  model: 'gpt-image-1.5',
  prompt: 'A futuristic city',
  quality: 'high',
  size: '1024x1536'
});

await fetch('https://ingest.fenra.io/usage/transactions', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
    'X-Api-Key': process.env.FENRA_API_KEY
  },
  body: JSON.stringify({
    provider: 'openai',
    model: 'gpt-image-1.5',
    usage: [{
      type: 'images',
      metrics: {
        generated: response.data.length,
        quality: 'high',  // Options: 'low', 'medium', 'high'
        width_px: 1024,
        height_px: 1536
      }
    }],
    context: {
      billable_customer_id: process.env.BILLABLE_CUSTOMER_ID
    }
  })
});

Audio (Whisper, TTS)

For Whisper transcription:

usage: [{
  type: 'audio_seconds',
  metrics: {
    input_seconds: audioDurationInSeconds,
    total_seconds: audioDurationInSeconds
  }
}]

For TTS:

usage: [{
  type: 'audio_seconds',
  metrics: {
    output_seconds: estimatedDuration,
    total_seconds: estimatedDuration
  }
}]

Supported Models

Fenra supports all OpenAI models. Common models include:

Model	Type	Usage Type
`gpt-4o`, `gpt-4o-mini`	Chat	`tokens`
`gpt-4-turbo`, `gpt-4`	Chat	`tokens`
`gpt-3.5-turbo`	Chat	`tokens`
`o1-preview`, `o1-mini`	Reasoning	`tokens` (with reasoning)
`o3`, `o3-mini`	Reasoning	`tokens` (with reasoning)
`dall-e-3`, `dall-e-2`	Image	`images`
`whisper-1`	Audio	`audio_seconds`
`tts-1`, `tts-1-hd`	Audio	`audio_seconds`

Next Steps

Set up alerts to get notified when OpenAI spending exceeds thresholds
View the dashboard to see your OpenAI costs

Get Started

Platform

Integrations

Guides

Chat Completions (GPT-4, GPT-3.5, o1, o3)

Prompt Caching

Reasoning Models (o1, o3)

Batch and Priority Tiers

Multimodal Tokens (Realtime API)

Image Generation (DALL-E)

GPT Image Models

Audio (Whisper, TTS)

Supported Models

Next Steps

​Chat Completions (GPT-4, GPT-3.5, o1, o3)

​Prompt Caching

​Reasoning Models (o1, o3)

​Batch and Priority Tiers

​Multimodal Tokens (Realtime API)

​Image Generation (DALL-E)

​GPT Image Models

​Audio (Whisper, TTS)

​Supported Models

​Next Steps

Chat Completions (GPT-4, GPT-3.5, o1, o3)

Prompt Caching

Reasoning Models (o1, o3)

Batch and Priority Tiers

Multimodal Tokens (Realtime API)

Image Generation (DALL-E)

GPT Image Models

Audio (Whisper, TTS)

Supported Models

Next Steps